





'''simple docstring'''

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


a   	=    {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
   assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
   if args.mlm:
      assert os.path.isfile(args.token_counts   )
      assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
   else:
      assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

   assert args.teacher_type == args.student_type or (
       args.student_type == "distilbert" and args.teacher_type == "bert"
   )
   assert os.path.isfile(args.student_config   )
   if args.student_pretrained_weights is not None:
      assert os.path.isfile(args.student_pretrained_weights   )

   if args.freeze_token_type_embds:
      assert args.student_type in ["roberta"]

   assert args.alpha_ce >= 0.0
   assert args.alpha_mlm >= 0.0
   assert args.alpha_clm >= 0.0
   assert args.alpha_mse >= 0.0
   assert args.alpha_cos >= 0.0
   assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   if args.student_type == "roberta":
      __SCREAMING_SNAKE_CASE   				=							False
   elif args.student_type == "gpt2":
      __SCREAMING_SNAKE_CASE   				=							False

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   if args.student_type == "roberta":
      __SCREAMING_SNAKE_CASE   				=							False

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser(description="""Training"""   )
   parser.add_argument("""--force"""				,						action="""store_true"""				,						help="""Overwrite dump_path if it already exists."""   )

   parser.add_argument(
       """--dump_path"""				,						type=__snake_case				,						required=__snake_case				,						help="""The output directory (log, checkpoints, parameters, etc.)"""   )
   parser.add_argument(
       """--data_file"""				,						type=__snake_case				,						required=__snake_case				,						help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""				,						)

   parser.add_argument(
       """--student_type"""				,						type=__snake_case				,						choices=["""distilbert""", """roberta""", """gpt2"""]				,						required=__snake_case				,						help="""The student type (DistilBERT, RoBERTa)."""				,						)
   parser.add_argument("""--student_config"""				,						type=__snake_case				,						required=__snake_case				,						help="""Path to the student configuration."""   )
   parser.add_argument(
       """--student_pretrained_weights"""				,						default=__snake_case				,						type=__snake_case				,						help="""Load student initialization checkpoint."""   )

   parser.add_argument(
       """--teacher_type"""				,						choices=["""bert""", """roberta""", """gpt2"""]				,						required=__snake_case				,						help="""Teacher type (BERT, RoBERTa)."""   )
   parser.add_argument("""--teacher_name"""				,						type=__snake_case				,						required=__snake_case				,						help="""The teacher model."""   )

   parser.add_argument("""--temperature"""				,						default=2.0				,						type=__snake_case				,						help="""Temperature for the softmax temperature."""   )
   parser.add_argument(
       """--alpha_ce"""				,						default=0.5				,						type=__snake_case				,						help="""Linear weight for the distillation loss. Must be >=0."""   )
   parser.add_argument(
       """--alpha_mlm"""				,						default=0.0				,						type=__snake_case				,						help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""				,						)
   parser.add_argument("""--alpha_clm"""				,						default=0.5				,						type=__snake_case				,						help="""Linear weight for the CLM loss. Must be >=0."""   )
   parser.add_argument("""--alpha_mse"""				,						default=0.0				,						type=__snake_case				,						help="""Linear weight of the MSE loss. Must be >=0."""   )
   parser.add_argument(
       """--alpha_cos"""				,						default=0.0				,						type=__snake_case				,						help="""Linear weight of the cosine embedding loss. Must be >=0."""   )

   parser.add_argument(
       """--mlm"""				,						action="""store_true"""				,						help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""   )
   parser.add_argument(
       """--mlm_mask_prop"""				,						default=0.1_5				,						type=__snake_case				,						help="""Proportion of tokens for which we need to make a prediction."""				,						)
   parser.add_argument("""--word_mask"""				,						default=0.8				,						type=__snake_case				,						help="""Proportion of tokens to mask out."""   )
   parser.add_argument("""--word_keep"""				,						default=0.1				,						type=__snake_case				,						help="""Proportion of tokens to keep."""   )
   parser.add_argument("""--word_rand"""				,						default=0.1				,						type=__snake_case				,						help="""Proportion of tokens to randomly replace."""   )
   parser.add_argument(
       """--mlm_smoothing"""				,						default=0.7				,						type=__snake_case				,						help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""				,						)
   parser.add_argument("""--token_counts"""				,						type=__snake_case				,						help="""The token counts in the data_file for MLM."""   )

   parser.add_argument(
       """--restrict_ce_to_mask"""				,						action="""store_true"""				,						help="""If true, compute the distillation loss only the [MLM] prediction distribution."""				,						)
   parser.add_argument(
       """--freeze_pos_embs"""				,						action="""store_true"""				,						help="""Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."""				,						)
   parser.add_argument(
       """--freeze_token_type_embds"""				,						action="""store_true"""				,						help="""Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."""				,						)

   parser.add_argument("""--n_epoch"""				,						type=__snake_case				,						default=3				,						help="""Number of pass on the whole dataset."""   )
   parser.add_argument("""--batch_size"""				,						type=__snake_case				,						default=5				,						help="""Batch size (for each process)."""   )
   parser.add_argument(
       """--group_by_size"""				,						action="""store_false"""				,						help="""If true, group sequences that have similar length into the same batch. Default is true."""				,						)

   parser.add_argument(
       """--gradient_accumulation_steps"""				,						type=__snake_case				,						default=50				,						help="""Gradient accumulation for larger training batches."""				,						)
   parser.add_argument("""--warmup_prop"""				,						default=0.0_5				,						type=__snake_case				,						help="""Linear warmup proportion."""   )
   parser.add_argument("""--weight_decay"""				,						default=0.0				,						type=__snake_case				,						help="""Weight decay if we apply some."""   )
   parser.add_argument("""--learning_rate"""				,						default=5e-4				,						type=__snake_case				,						help="""The initial learning rate for Adam."""   )
   parser.add_argument("""--adam_epsilon"""				,						default=1e-6				,						type=__snake_case				,						help="""Epsilon for Adam optimizer."""   )
   parser.add_argument("""--max_grad_norm"""				,						default=5.0				,						type=__snake_case				,						help="""Max gradient norm."""   )
   parser.add_argument("""--initializer_range"""				,						default=0.0_2				,						type=__snake_case				,						help="""Random initialization range."""   )

   parser.add_argument(
       """--fp16"""				,						action="""store_true"""				,						help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""				,						)
   parser.add_argument(
       """--fp16_opt_level"""				,						type=__snake_case				,						default="""O1"""				,						help=(
           """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
           """See details at https://nvidia.github.io/apex/amp.html"""
       )				,						)
   parser.add_argument("""--n_gpu"""				,						type=__snake_case				,						default=1				,						help="""Number of GPUs in the node."""   )
   parser.add_argument("""--local_rank"""				,						type=__snake_case				,						default=-1				,						help="""Distributed training - Local rank"""   )
   parser.add_argument("""--seed"""				,						type=__snake_case				,						default=56				,						help="""Random seed"""   )

   parser.add_argument("""--log_interval"""				,						type=__snake_case				,						default=500				,						help="""Tensorboard logging interval."""   )
   parser.add_argument("""--checkpoint_interval"""				,						type=__snake_case				,						default=4000				,						help="""Checkpoint interval."""   )
   __SCREAMING_SNAKE_CASE   				=							parser.parse_args()
   sanity_checks(__snake_case   )

   # ARGS #
   init_gpu_params(__snake_case   )
   set_seed(__snake_case   )
   if args.is_master:
      if os.path.exists(args.dump_path   ):
         if not args.force:
            raise ValueError(
                f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                """ itUse `--force` if you want to overwrite it"""   )
         else:
            shutil.rmtree(args.dump_path   )

      if not os.path.exists(args.dump_path   ):
         os.makedirs(args.dump_path   )
      logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""   )

      # SAVE PARAMS #
      logger.info(f"""Param: {args}"""   )
      with open(os.path.join(args.dump_path				,						"""parameters.json"""   )				,						"""w"""   ) as f:
         json.dump(vars(__snake_case   )				,						__snake_case				,						indent=4   )
      git_log(args.dump_path   )

   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							MODEL_CLASSES[args.student_type]
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							MODEL_CLASSES[args.teacher_type]

   # TOKENIZER #
   __SCREAMING_SNAKE_CASE   				=							teacher_tokenizer_class.from_pretrained(args.teacher_name   )
   __SCREAMING_SNAKE_CASE   				=							{}
   for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
      __SCREAMING_SNAKE_CASE   				=							tokenizer.all_special_tokens.index(__snake_case   )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.all_special_ids[idx]
   logger.info(f"""Special tokens {special_tok_ids}"""   )
   __SCREAMING_SNAKE_CASE   				=							special_tok_ids
   __SCREAMING_SNAKE_CASE   				=							tokenizer.max_model_input_sizes[args.teacher_name]

   # DATA LOADER #
   logger.info(f"""Loading data from {args.data_file}"""   )
   with open(args.data_file				,						"""rb"""   ) as fp:
      __SCREAMING_SNAKE_CASE   				=							pickle.load(__snake_case   )

   if args.mlm:
      logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""   )
      with open(args.token_counts				,						"""rb"""   ) as fp:
         __SCREAMING_SNAKE_CASE   				=							pickle.load(__snake_case   )

      __SCREAMING_SNAKE_CASE   				=							np.maximum(__snake_case				,						1   ) ** -args.mlm_smoothing
      for idx in special_tok_ids.values():
         __SCREAMING_SNAKE_CASE   				=							0.0  # do not predict special tokens
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(__snake_case   )
   else:
      __SCREAMING_SNAKE_CASE   				=							None

   __SCREAMING_SNAKE_CASE   				=							LmSeqsDataset(params=__snake_case				,						data=__snake_case   )
   logger.info("""Data loader created."""   )

   # STUDENT #
   logger.info(f"""Loading student config from {args.student_config}"""   )
   __SCREAMING_SNAKE_CASE   				=							student_config_class.from_pretrained(args.student_config   )
   __SCREAMING_SNAKE_CASE   				=							True

   if args.student_pretrained_weights is not None:
      logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""   )
      __SCREAMING_SNAKE_CASE   				=							student_model_class.from_pretrained(args.student_pretrained_weights				,						config=__snake_case   )
   else:
      __SCREAMING_SNAKE_CASE   				=							student_model_class(__snake_case   )

   if args.n_gpu > 0:
      student.to(f"""cuda:{args.local_rank}"""   )
   logger.info("""Student loaded."""   )

   # TEACHER #
   __SCREAMING_SNAKE_CASE   				=							teacher_model_class.from_pretrained(args.teacher_name				,						output_hidden_states=__snake_case   )
   if args.n_gpu > 0:
      teacher.to(f"""cuda:{args.local_rank}"""   )
   logger.info(f"""Teacher loaded from {args.teacher_name}."""   )

   # FREEZING #
   if args.freeze_pos_embs:
      freeze_pos_embeddings(__snake_case				,						__snake_case   )
   if args.freeze_token_type_embds:
      freeze_token_type_embeddings(__snake_case				,						__snake_case   )

   # SANITY CHECKS #
   assert student.config.vocab_size == teacher.config.vocab_size
   assert student.config.hidden_size == teacher.config.hidden_size
   assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
   if args.mlm:
      assert token_probs.size(0   ) == stu_architecture_config.vocab_size

   # DISTILLER #
   torch.cuda.empty_cache()
   __SCREAMING_SNAKE_CASE   				=							Distiller(
       params=__snake_case				,						dataset=__snake_case				,						token_probs=__snake_case				,						student=__snake_case				,						teacher=__snake_case   )
   distiller.train()
   logger.info("""Let\'s go get some drinks."""   )


if __name__ == "__main__":
    main()













"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()